# CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

arXiv: https://arxiv.org/abs/2406.10840 · HTML: https://mlsanity.com/paper/2406.10840/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 74 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 30 of 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Torsional Diffusion for Molecular Conformer Generation](https://mlsanity.com/paper/2206.01729/) (2022)
- [GEOM: Energy-annotated molecular conformations for property prediction and molecular generation](https://mlsanity.com/paper/2006.05531/) (2020)
- [Structure-based Drug Design with Equivariant Diffusion Models](https://mlsanity.com/paper/2210.13695/) (2022)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction](https://mlsanity.com/paper/2303.03543/) (2023)
- [E(n) Equivariant Normalizing Flows](https://mlsanity.com/paper/2105.09016/) (2021)
- [A 3D Generative Model for Structure-Based Drug Design](https://mlsanity.com/paper/2203.10446/) (2022)
- [Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design](https://mlsanity.com/paper/2210.05274/) (2022)
- [Generating 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2204.09410/) (2022)
- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design](https://mlsanity.com/paper/2403.07902/) (2024)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [ATOM3D: Tasks On Molecules in Three Dimensions](https://mlsanity.com/paper/2012.04035/) (2020)
- [DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2211.11214/) (2022)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space](https://mlsanity.com/paper/2404.12141/) (2024)
- [3D molecule generation by denoising voxel grids](https://mlsanity.com/paper/2306.07473/) (2023)
- [Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models](https://mlsanity.com/paper/2010.14442/) (2020)
- [Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration](https://mlsanity.com/paper/2306.13769/) (2023)
- [Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design](https://mlsanity.com/paper/2210.04893/) (2022)
- [Structure-based drug design by denoising voxel grids](https://mlsanity.com/paper/2405.03961/) (2024)
- [Improving Molecular Design by Stochastic Iterative Target Augmentation](https://mlsanity.com/paper/2002.04720/) (2020)
- [Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks](https://mlsanity.com/paper/2403.15441/) (2024)

## Built on by (most cited first, 19 of 19)

- [UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design](https://mlsanity.com/paper/2503.19300/) (2025)
- [MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs](https://mlsanity.com/paper/2508.02066/) (2025)
- [Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks](https://mlsanity.com/paper/2411.13280/) (2024)
- [Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number](https://mlsanity.com/paper/2509.01486/) (2025)
- [Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences](https://mlsanity.com/paper/2606.16905/) (2026)
- [Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Design](https://mlsanity.com/paper/2601.14283/) (2026)
- [READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design](https://mlsanity.com/paper/2506.14488/) (2025)
- [MolPIF: A Parameter Interpolation Flow Model for Molecule Generation](https://mlsanity.com/paper/2507.13762/) (2025)
- [IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data](https://mlsanity.com/paper/2508.10775/) (2025)
- [FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design](https://mlsanity.com/paper/2603.05567/) (2026)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules](https://mlsanity.com/paper/2607.00464/) (2026)
- [MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation](https://mlsanity.com/paper/2507.07201/) (2025)
- [SculptDrug : A Spatial Condition-Aware Bayesian Flow Model for Structure-based Drug Design](https://mlsanity.com/paper/2511.12489/) (2025)
- [EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design](https://mlsanity.com/paper/2601.22466/) (2026)
- [Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance](https://mlsanity.com/paper/2606.27824/) (2026)
- [Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints](https://mlsanity.com/paper/2607.18144/) (2026)
- [Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation](https://mlsanity.com/paper/2608.31009/) (2026)
- [Budgeted Quotient-Residual Guidance for Frozen Pocket-Conditioned Molecular Diffusion](https://mlsanity.com/paper/2609.31222/) (2026)

## Abstract (arXiv)

Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights. To address this dilemma, we propose CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a generative heterogeneous graph completion, analogous to fill-in-the-blank of the 3D complex binding graph. By categorizing existing methods based on their attributes, CBGBench facilitates a modular and extensible framework that implements various cutting-edge methods. Secondly, a single task on textitde novo molecule generation can hardly reflect their capabilities. To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks. These tasks include the generative designation of textitde novo molecules, linkers, fragments, scaffolds, and sidechains, all conditioned on the structures of protein pockets. Our evaluations are conducted with fairness, encompassing comprehensive perspectives on interaction, chemical properties, geometry authenticity, and substructure validity. We further provide the pre-trained versions of the state-of-the-art models and deep insights with analysis from empirical studies. The codebase for CBGBench is publicly accessible at urlhttps://github.com/Edapinenut/CBGBench.